173 results about "Bayesian neural networks" patented technology

The invention relates to the technical field of simulation model optimization, and discloses an aero-engine model Bayesian optimization method for quantizing uncertainty, and the method comprises the steps: building a probability mapping relation from a component index to an output response through constructing a Bayesian neural network agent model based on a probability weight coefficient; and by taking the difference between the output response and the corresponding complete machine test data as a multi-objective loss function and taking the minimization of the multi-objective loss function as an optimization objective, optimizing the component indexes by adopting a Bayesian optimization method based on a Gaussian process to obtain an optimal component index combination. Not only is a nonlinear relationship between high-dimensional parameters and simulation-test deviation accurately modeled through a neural network, but also efficient search of a parameter space is realized through a Gaussian process. The technical problems that when a traditional optimization method is used for processing the high-dimensional, strong-nonlinearity and multi-parameter coupling complex optimization problem of the aero-engine, the calculation efficiency is low, local optimum is prone to occurring, and result uncertainty cannot be quantified are solved.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention relates to the technical field of artificial intelligence, in particular to a multi-modal emotion recognition and interaction adjusting system and method based on uncertainty evaluation, and solves the defects of uncertainty processing, robustness of interaction strategies, complementary information mining degree among modals and the like in the man-machine interaction process in the prior art. Depth application finiteness is caused by lack of modeling for feature uncertainty after fusion. The uncertainty of an emotion recognition result is quantified through technologies such as multi-modal feature fusion and Bayesian neural network / model integration, an interaction strategy is dynamically adjusted according to an uncertainty score, emotion clarification or conservative response is triggered in a high-uncertainty scene, the robustness of human-computer interaction and the user experience are improved, and the user experience is improved. The method is suitable for intelligent customer service, government affair consultation, medical inquiry and other scenes with high requirements for emotion interaction accuracy.

The invention provides a method for predicting the residual life of a power transformer based on digital-analog fusion, and belongs to the technical field of transformer detection.The method comprises the steps that multi-dimensional sensor data of the power transformer is collected, wavelet transform preprocessing is conducted, a normalized data matrix is established, a physical equation is established, and a deterministic physical model is formed; a data-driven model is established based on an improved adaptive multi-scale network to realize multi-scale feature adaptive extraction, a topological phase change algorithm is introduced to identify key transition points in an aging process, and a deterministic physical model and the data-driven model are fused to establish a digital-analog fusion prediction framework. A generative adversarial network is adopted to perform data enhancement to solve the problem of scarcity of fault samples, a Bayesian neural network and a Monte Carlo random inactivation technology are utilized to construct an uncertainty quantization framework to output a residual life prediction value and a confidence interval thereof, and the technical problem that the prediction precision of the residual life of the transformer is not high is solved.

The invention relates to the technical field of numerical control machining process optimization, in particular to an aluminum alloy milling parameter optimization method based on an improved Bayesian neural network, and the method comprises the following steps: S1, building an aluminum alloy thin-wall part milling finite element model based on a Johnson-Cook constitutive model; s2, carrying out a single-factor milling experiment, and verifying the reliability of the finite element model; s3, constructing a Bayesian neural network model fused with a multi-head attention mechanism, and establishing a mapping relation between process parameters and surface quality indexes; s4, establishing a multi-objective optimization model based on the MHA-BNN prediction model, and solving by adopting an NSGA-II algorithm; and S5, performing experimental verification and analysis on an optimization result. According to the method, a multi-head attention mechanism is introduced into a Bayesian neural network, and a milled surface roughness and residual stress probability prediction model is constructed; and a multi-objective optimization framework fusing physical constraint and data driving is established, collaborative optimization of the surface roughness, the residual stress and the processing efficiency is realized, and meanwhile, the problem that a traditional deep learning method is insufficient in prediction precision under a small sample condition is solved.

The invention provides a mechanical arm control method and system based on mechanism-Bayesian joint modeling, and relates to the technical field of robot control, and the method comprises the steps that firstly, a mechanical arm mechanism model is constructed, parameters of the mechanical arm mechanism model are estimated, and preliminary dynamics prediction is obtained; then, combining with motor driving torque observation data, establishing a random mathematical model of mechanism model residual errors, and decomposing the random mathematical model into deterministic and random parts; carrying out probability learning on the residual error by utilizing a Bayesian neural network, and outputting a prediction mean value and a variance of the residual error; in combination with preliminary dynamic prediction and residual information, constructing a data-driven uncertainty adaptive control law without dependence of an acceleration signal, and carrying out random stability analysis; and a stable joint driving torque instruction is generated, and high-precision trajectory tracking of the mechanical arm is achieved. According to the method, the interpretability of the mechanism model and the high adaptability of the data driving model are combined, the control precision and flexibility are effectively considered, and the robustness and reliability of the mechanical arm in the complex dynamic environment are improved.

The invention discloses a mechanical equipment residual service life prediction and uncertainty quantitative analysis method and device. The method comprises the following steps: acquiring multi-dimensional time sequence sensor data generated by mechanical equipment to be predicted in an operation process; inputting the multi-dimensional time sequence sensor data into a pre-trained physical constraint Bayesian neural network model; wherein the physical constraint Bayesian neural network model comprises a segmented bidirectional long short-term memory network feature extraction module, a hierarchical gating recursive regression network degradation dynamic modeling module and a Bayesian reasoning and physical constraint fusion regression module which are connected in sequence; the segmented bidirectional long-short-term memory network feature extraction module is used for processing input multi-dimensional time sequence sensor data and outputting hidden feature vectors representing local degradation dynamics of equipment; the hierarchical gating recursive regression network degradation dynamic modeling module is used for processing hidden feature vectors and capturing global degradation dynamic features through a complex numerical value hidden state updating mechanism; the Bayesian reasoning and physical constraint fusion regression module is used for carrying out Weibull distribution parameter regression based on the global degradation dynamic characteristics, introducing a deep implicit physical residual constraint and outputting a probability distribution parameter of the residual service life; and based on the probability distribution parameters, generating a residual service life prediction result and uncertainty quantitative information of the mechanical equipment.

The invention relates to a Bayesian KAN fused aircraft rapid multidisciplinary uncertainty analysis method, and belongs to the technical field of spacecraft manufacturing and application. According to the method, Bayesian learning is combined with a Colmogorov-Arnodel network, a Bayesian KAN neural network which can cope with high-dimensional input uncertainty, can quantify model cognitive uncertainty and is higher in fitting capacity is provided, a BKAN network agent model is constructed for a subsubject analysis model, time-consuming numerical simulation of each subsubject is replaced, and the calculation amount is reduced; with reference to a basic framework of a global sensitivity equation GSE of a deterministic field coupling multidisciplinary system, input random input and model cognition uncertainty are introduced, and expressions of mean values and covariances of disciplinary coupling variables and system responses are deduced on the GSE framework; a rapid semi-analytical multidisciplinary random and model cognition hybrid uncertainty propagation method is established, efficient multidisciplinary uncertainty analysis is achieved, and the problems that an existing method is poor in precision and large in calculation amount are solved.

The invention discloses a power load prediction method, and the method comprises the steps: firstly solving an extreme event data sparsity problem through a generative adversarial network, and constructing an event time sequence library through a time sequence anomaly detection algorithm; then analyzing the causal relationship between the event and the load by applying a causal discovery algorithm, and converting prediction output into probability distribution by adopting a Bayesian neural network to quantify uncertainty; constructing a prediction model triggered by an event, and generating a multi-time scale probability prediction interval; and finally, generating a multi-scene prediction result through Monte Carlo simulation, quantifying the system recovery capability in combination with a toughness index, and integrating the system recovery capability to a decision support system to generate a risk response scheme. According to the method, the accuracy and robustness of load prediction under the extreme climate are remarkably improved, full-chain risk insight from early warning to recovery is realized, and prospective decision support is provided for safe operation of a power system.

The invention relates to the technical field of oil and gas field development, in particular to a fracture parameter inversion method based on a Bayesian neural network, which comprises the following steps: establishing a bottom hole net pressure conversion model based on an actual construction curve, and drawing a bottom hole net pressure curve; calculating a bottom hole net pressure index sequence and a corresponding time sequence; establishing a shaft bottom crack extension mode judgment criterion on the basis of a classic double logarithmic curve analysis method; taking the net pressure index sequence and the time sequence obtained in the previous step as input data, combining actual physical parameter constraints, and establishing an inversion fracture parameter model based on a Bayesian neural network; and inputting the pressure index sequence to be inverted into the Bayesian neural network model to obtain a specific fracture parameter inversion result. According to the technical scheme, the confidence interval of the prediction result can be given, the prediction result and uncertainty quantification capability can be synchronously provided, and the reliability and decision support value of the inversion result are greatly improved.

The invention provides an electric quantity prediction method and system fusing physical constraint factors, and relates to the technical field of electric quantity prediction. Historical load, weather, electricity price and calendar data are collected, and a key feature set is constructed through preprocessing and feature selection; a prediction model with the physical information neural network as the core is constructed, the prediction model comprises a recursion sub-module used for short-term prediction and a trend sub-module used for long-term prediction, and a physical constraint loss item based on a physical rule is introduced into model training so as to enhance the generalization ability; a multi-time granularity modeling framework is adopted, uncertainty quantization is achieved through a Monte Carlo Dropout or Bayesian neural network, and a confidence interval of a predicted value is output; and finally, causal reasoning is carried out through a Shapley value algorithm and anti-fact simulation, and key influence factors are identified. According to the method, the precision, stability and interpretability of electric quantity prediction are effectively improved, and reliable support is provided for power grid dispatching and decision making.

The invention discloses a super-set deterministic weather forecast method and device based on machine learning, and the method comprises the steps: obtaining multi-source meteorological data of a target region, carrying out the meshing of the multi-source meteorological data, carrying out the historical static feature analysis and dynamic feature analysis of the meshing features, obtaining the feature weight of each mode, and carrying out the recognition of the multi-source meteorological data. The method comprises the following steps: constructing a prediction sub-model according to an extreme event, obtaining enhanced numerical prediction data, obtaining posterior probability distribution of grid points through a conditional generative adversarial network and a Bayesian neural network based on the numerical prediction data, a gridding feature and a feature weight, taking a maximum probability value as a deterministic weather forecast, and calculating a confidence interval. And obtaining a joint probability product including wind speed and rainfall joint distribution and the characteristic contribution degree. According to the method, numerical forecasting set products of different mode centers are utilized to fuse probability forecasting information, deterministic weather forecasting is obtained, smoothing of extreme events is reduced, deterministic maximum value output is provided, and meanwhile good interpretability is achieved.

The invention discloses a complex equipment probability fatigue life prediction method based on a physical information neural network, and belongs to the field of complex equipment fatigue analysis, and the method comprises the steps: firstly, grouping the fatigue life data of a complex equipment material according to stress, carrying out the self-adaptive hybrid uncertainty quantization through non-parameter probability estimation, linear regression and maximum entropy modeling, and carrying out the prediction of the fatigue life data of the complex equipment material; thirdly, training a physical guidance neural network based on standard deviation data obtained through fitting, supplementing standard deviation under missing or limited data stress, training a Bayesian physical information neural network based on supplementary standard deviation data and fatigue life data, and sampling the pre-trained Bayesian neural network for multiple times to obtain a PSN curve; the method is used for probabilistic fatigue life prediction of complex equipment. According to the method, the accuracy and the stability of probability fatigue life prediction of the complex equipment are remarkably improved, and the physical consistency of prediction results is ensured.

The invention relates to a transformer abnormal sound source positioning method and system, and belongs to the technical field of power equipment state evaluation, and the method comprises the steps: constructing a Bayesian neural network embedded with a voiceprint physical mechanism, and enabling a sound wave propagation equation to serve as a physical constraint to be embedded into the Bayesian neural network; reconstructing the voiceprint signal by adopting a compressed sensing technology to obtain a reconstructed voiceprint field; designing a multi-task objective function including data fitting, physical constraint and positioning loss, and optimizing data fitting, physical constraint and positioning precision to obtain a trained Bayesian neural network; based on a gradient sound source inversion positioning algorithm and the trained Bayesian neural network, sparse regularization is combined to obtain a prediction result of accurate positioning; and a prediction result is visualized to a three-dimensional model of the transformer, and the position of an abnormal sound source is visually displayed. According to the method, the limitation of a traditional method in a complex environment is overcome, and high-precision and high-robustness transformer abnormal sound source positioning is realized.

The invention provides a bridge life prediction method and system based on a physical information neural network and fusing a physical degradation mechanism and monitoring data, realizes reliable and real-time prediction of the residual life of a bridge, and relates to the technical field of bridge structure health monitoring. The method comprises the following steps: constructing a bridge real-time feature tensor, forming a bridge real-time feature tensor with uniform space-time alignment, constructing a physical information neural network model by taking a space-time coordinate (x, t) in the formed bridge real-time feature tensor as network input, and outputting endogenous physical field data for representing a degeneration state of a detected bridge; retraining the physical information neural network model; and taking endogenous physical field data output by the retrained physical information neural network model as input, and feeding the endogenous physical field data into a pre-trained Bayesian neural network model to realize uncertainty quantification and prediction of the service life of the tested bridge.

The invention provides a power battery fault prediction method based on an LSTM-BNN model, and relates to the field of battery safety management and fault detection. The invention aims to improve the capability of predicting the battery fault and provide reliable uncertainty quantitative indexes and management decision support in a mode of combining deep learning and the Bayesian theory. The method comprises the core steps of data acquisition and database establishment, data preprocessing and fault labeling, long short term memory (LSTM) network time sequence feature extraction, Bayesian neural network (BNN) fault prediction, uncertainty quantification, risk assessment and early warning and the like. The method can improve the accuracy and reliability of fault detection, can be widely applied to the fields of new energy automobiles, unmanned aerial vehicles, portable electronic equipment and the like, and remarkably improves the safety and prolongs the service life of the power battery.

The invention provides a river flow online measurement and calculation method based on multi-source data fusion, and belongs to the technical field of river flow measurement. A Bayesian neural network and active learning collaborative hydrological memory reconstruction model is adopted to carry out high-confidence data interpolation on a sensor failure period and quantify uncertainty, fractional calculus is introduced to model a river memory effect, and flow evolution is analyzed through a fractional order water balance equation. And selecting a steady-state or non-constant flow calculation mode according to the flow change rate, inverting an optimal flow field for the non-constant flow by adopting a four-dimensional variational data assimilation method in combination with regularization constraint and time smoothing constraint, and outputting a flow measurement result and an uncertainty quantitative index. The technical problems that flow measurement and calculation data are missing and accurate interpolation is difficult due to sensor failure under the extreme hydrological condition are solved.

The invention relates to the technical field of artificial intelligence, and discloses a Bayesian neural network method fused with uncertainty quantization and a system thereof, the method comprises five steps of probability weight reconstruction modulo, variational posterior inference, re-parameterization sampling, multi-scale uncertainty quantization and adaptive rejection decision, the network weight reconstruction modulo is probability distribution, and the probability distribution of the network weight reconstruction modulo is improved. Sparse induction is realized by adopting scale Gaussian mixture prior, calculation complexity is reduced through a local re-parameterization technique, reasoning time consumption is controlled within two times of a deterministic network, a multi-scale uncertainty quantification module quantifies cognitive uncertainty by counting output variance of multiple forward propagation, and the accuracy of reasoning is improved. According to the method, the technical problem that a deep learning model lacks reliable prediction confidence estimation is effectively solved, and the misdiagnosis sample omission ratio in a medical image classification task is reduced.

The invention relates to the technical field of cloud computing and multi-source data fusion, and discloses a multi-source data fusion method and system based on cloud computing, and the method comprises the steps: obtaining source scene data and target scene data, carrying out the modeling of the uncertainty distribution of a source scene through a Bayesian neural network, and obtaining the target scene data; network parameters with probability distribution are obtained; calculating the domain difference between the source scene and the target scene, and determining the distribution offset degree; uncertainty perception knowledge migration is executed, parameter probability distribution of a source scene model is migrated to a target scene, and the migration intensity is adaptively determined by domain differences; using the migration result to generate a fusion result with a confidence interval, and providing decision reliability evaluation; on the basis of actual feedback of the target scene, uncertainty model parameters are updated, and a migration strategy is optimized; according to the multi-source data fusion method for Bayesian uncertainty migration, knowledge migration can be carried out while uncertainty is reserved and adjusted.

The invention discloses a soil erosion resistance prediction method and system based on earth surface parameters, and relates to the technical field of soil erosion and water and soil conservation monitoring and prediction, and the method comprises the steps of earth surface parameter collection, parameter preprocessing, erosion response modeling, erosion resistance threshold tensor inversion and erosion resistance prediction. Collecting multi-source earth surface parameter data; secondly, constructing a unified earth surface parameter feature set; obtaining erosion response simulation data by adopting an erosion response modeling method combining physical process simulation and lightweight machine learning residual correction; an explicit Bayesian neural network and a variational reasoning framework are constructed, observation data are combined, and a four-dimensional anti-corrosion threshold tensor is obtained through inversion; introducing a double-branch space-time multi-task fusion prediction model of an anti-erosion threshold tensor, and outputting a prediction value and a prediction grade of the soil anti-erosion capability; according to the scheme, dynamic and spatial prediction of the erosion resistance of the soil can be realized, and a scientific basis is provided for water and soil conservation and ecological environment management.

The invention relates to the technical field of first-aid repair tower safety assessment, and discloses a first-aid repair tower safety risk assessment method and device, and the method comprises the steps: building a first-aid repair tower assessment model based on a Bayesian neural network, and enabling the first-aid repair tower assessment model to comprise an input layer node, a middle layer node and an output layer node, the input layer node represents a risk cause of the first-aid repair tower, the middle layer node represents a middle assessment node of the risk of the first-aid repair tower, and the output layer node represents a risk assessment target of the first-aid repair tower; acquiring first-aid repair tower data and environment data; inputting the first-aid repair tower data and the environment data into the first-aid repair tower evaluation model for probabilistic reasoning to obtain state probability distribution of the output layer nodes; and performing risk assessment on the first-aid repair tower according to the state probability distribution of the output layer nodes.

The invention relates to the technical field of wind turbine generator fault early warning, and discloses a wind turbine generator fault early warning method and system based on multi-modal data fusion, and the method comprises the steps: collecting the data of a multi-modal sensor, and carrying out the time-space alignment preprocessing; multi-modal features are extracted through variational mode decomposition, STL decomposition and other methods, and cross-modal fusion is achieved through dimension adaptive projection and a multi-head attention mechanism; calculating a dynamic weight based on three factors of data quality, fault type correlation and information gain, and carrying out weighted fusion; constructing a dynamic unit topological graph, and capturing cross-unit association features by using a space-time diagram convolutional network; long-time early warning with confidence is realized through double-branch gating fusion in combination with a Bayesian neural network; a multi-label classification identification multi-fault mode is adopted, and an operation and maintenance decision is optimized through an adaptive large neighborhood search algorithm. According to the method, the long early warning window of the offshore wind turbine generator can be realized, and uncertainty quantification and intelligent operation and maintenance decision support are provided.

The invention discloses a Bayesian neural network optimization method using device heterogeneity and a Bayesian neural network architecture thereof, and belongs to the technical field of artificial intelligence hardware. The method comprises the following steps: constructing a device array with inherent non-uniform response characteristics; addressing and dynamic selection of a specific device are performed through a selection circuit; non-uniform response generated by selecting a specific device is used as a natural source of Bayesian neural network weight distribution; through dynamic sampling of different device combinations, weight random distribution sampling is obtained, and Bayesian neural network optimization is realized. Inherent heterogeneity in a device manufacturing process is converted into a computing resource of a Bayesian neural network, a photoelectric detector array structure and a selection circuit are designed, and natural variability of photoelectric response between devices is used as a random source of Bayesian weight distribution, so that energy consumption and area overhead are remarkably reduced; bayesian neural network probabilistic reasoning is realized, and the classification precision and the quantification capability of the Bayesian neural network are improved.

The invention discloses an integrated energy low-carbon operation model construction method and device and a management and control system, and the method comprises the steps: analyzing the operation state and carbon emission condition of an integrated energy system based on a constructed system directed graph, and giving an operation constraint and a carbon emission constraint; determining a system action space and a system reward function based on the current operation state and the scheduling operation of the integrated energy system; fusing the initial deep learning model, the system action space and the system reward function, analyzing state distribution of expected cumulative reward values corresponding to different system actions and different system states, constructing a deep learning model based on a Bayesian neural network, training the deep learning model based on the Bayesian neural network until convergence, and obtaining a final deep learning model; and obtaining a low-carbon operation model. The deep learning model based on the Bayesian neural network is introduced, topology change detection is analyzed, addition and deletion of nodes and lines can be rapidly recognized, real-time updating is achieved, and the operation efficiency is improved while the stability of the comprehensive energy system is kept.

The invention discloses a cardinal number estimation method based on a bidirectional long-short term memory network and ensemble learning, and aims to solve the limitation of a traditional cardinal number estimation method and an existing learning type method in accuracy and efficiency. According to the method, the structure, connection, operation and filtering condition information of the query plan tree is efficiently extracted through the four sub-encoders, the extracted feature sequence is compressed by using the bidirectional long-short-term memory network, the context dependency relationship between the nodes is effectively captured, and the model learning difficulty is reduced. Besides, a Bayesian neural network is introduced to be combined with an active learning strategy to screen and construct a plurality of high-value training data subsets, and a robust integrated model is trained on the basis to be used for cardinality estimation. According to the method, the cardinality estimation accuracy is remarkably improved on multiple data sets, and when complex query and multi-table connection scenes are processed, the method shows better comprehensive performance compared with other methods.

The invention discloses a multi-source soil water data fusion method based on a Bayesian neural network. Obtaining original data, and cleaning the original data to obtain a cleaned data set; on the basis of an inverse distance weighted interpolation method, calculating the value of data in the cleaning data set at the actual measurement site, and performing error correction on the multi-source soil water data by taking the soil water data of the actual measurement site as a reference to obtain a site data set; segmenting data points in the site data set to construct a training data set and a test data set; time characteristic data and geographical spatio-temporal data in the cleaned data set are used as input, a prior linear layer is embedded to generate a weight coefficient of multi-source soil aquatic product data, and a normal distribution prediction model is established for a weight generation process, network parameters and a prediction standard deviation based on a multilayer Bayesian method; and training parameters of the normal distribution prediction model, and judging the prediction precision of the normal distribution prediction model. According to the invention, reliability is ensured in a complex environment.

The invention discloses an abnormal account detection method and a related device, and relates to the field of computers, and the method comprises the steps: obtaining the multi-dimensional information of a to-be-detected account, extracting the features of the to-be-detected account from the multi-dimensional information, inputting the features of the to-be-detected account into a pre-trained Bayesian neural network detection model, and predicting an abnormal probability value that the to-be-detected account is an abnormal account and a confidence coefficient of the abnormal probability value, and determining whether the to-be-detected account is an abnormal account according to a size relationship between the abnormal probability value and a probability threshold value and a size relationship between the confidence coefficient and a confidence coefficient threshold value. According to the method, anomaly detection is carried out based on the multi-dimensional information of the to-be-detected account, and compared with a traditional single-dimensional detection method, the false alarm rate is effectively reduced; the abnormal probability value that the to-be-detected account is an abnormal account and the confidence corresponding to the abnormal probability value are predicted through the Bayesian neural network detection model, so that the recognition capability of a novel variation fraud means is improved, a complex association type fraud mode can be effectively dealt with, and the occurrence of missing detection conditions is reduced.

The invention provides a false comment prediction method and system based on a Bayesian multi-scale attention network, and relates to the technical field of network risk prediction, and the method comprises the steps: obtaining network comment core data; feature extraction and covariable design are carried out on the network comment core data, and covariables and time sequence comment data are fused to form a feature matrix; inputting the feature matrix into a comment prediction model, learning an association relationship from different perspectives by using a plurality of attention heads, and generating a context enhancement feature vector containing cross-product information; inputting the context enhancement feature vector into a Bayesian neural network, and outputting a predicted mean value and a logarithm standard deviation of the number of false comments in each time unit; and converting the logarithmic standard deviation into a non-negative standard deviation through an activation function, constructing Gaussian probability distribution of the number of false comments, and realizing prediction uncertainty quantization. The prediction precision and the risk reference value are remarkably improved.

This invention relates to the field of aero-engine maintenance engineering and quality assessment technology, and discloses an aero-engine overhaul assessment method based on a physical information Bayesian neural network. The method includes collecting multi-source heterogeneous data from maintenance and testing sites and converting it into key process indicator scores; extracting rotor dynamics, imbalance transmission, and empirical formula features to construct physical constraint regularization terms; establishing an initial Bayesian neural network containing deterministic and variational Bayesian layers; inputting the key process indicator scores into the network, and optimizing parameters by combining the physical constraint regularization terms with adaptive annealing and early stop mechanisms; performing multiple Monte Carlo samplings on the trained network to calculate the mean distribution of the output results and obtain distribution statistical characteristics; calculating the quality score, confidence interval, and attribution warning information based on the distribution statistical characteristics, and outputting an overhaul assessment report. This invention solves the problem that existing pure data models violate physical laws and cannot quantify confidence levels.

The application discloses a joint cartilage stress dynamic monitoring system based on flexible sensing and AI and belongs to the technical field of medical health monitoring.The application solves the problem that the prior art can only measure single-point pressure or strain and cannot obtain full-field three-dimensional stress distribution of a cartilage contact surface, and the system function stops at monitoring and does not form a monitoring-to-warning closed loop, quantifies uncertainty through a Bayesian neural network, provides a reliable basis for clinical decision-making, combines multidimensional biomechanical characteristics and an attention mechanism, accurately focuses on a stress key area, significantly improves the precision and individualization level of joint cartilage health evaluation, helps early detection and prevention of injury, classifies stress abnormalities, implements accurate early warning, and formulates an individualized treatment scheme according to uncertainty results and cartilage health indexes, and customizes a rehabilitation training plan according to user conditions, and improves the scientific nature of joint injury prevention and rehabilitation.